Overview

Dataset statistics

Number of variables31
Number of observations517
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)1.5%
Total size in memory125.3 KiB
Average record size in memory248.2 B

Variable types

Categorical22
Numeric9

Alerts

Dataset has 8 (1.5%) duplicate rowsDuplicates
FFMC is highly overall correlated with DMC and 4 other fieldsHigh correlation
DMC is highly overall correlated with FFMC and 6 other fieldsHigh correlation
DC is highly overall correlated with DMC and 7 other fieldsHigh correlation
ISI is highly overall correlated with FFMCHigh correlation
temp is highly overall correlated with FFMC and 3 other fieldsHigh correlation
RH is highly overall correlated with tempHigh correlation
wind is highly overall correlated with monthdecHigh correlation
month is highly overall correlated with DC and 12 other fieldsHigh correlation
day is highly overall correlated with dayfri and 6 other fieldsHigh correlation
dayfri is highly overall correlated with dayHigh correlation
daymon is highly overall correlated with dayHigh correlation
daysat is highly overall correlated with dayHigh correlation
daysun is highly overall correlated with dayHigh correlation
daythu is highly overall correlated with dayHigh correlation
daytue is highly overall correlated with dayHigh correlation
daywed is highly overall correlated with dayHigh correlation
monthapr is highly overall correlated with monthHigh correlation
monthaug is highly overall correlated with DMC and 3 other fieldsHigh correlation
monthdec is highly overall correlated with DC and 3 other fieldsHigh correlation
monthfeb is highly overall correlated with FFMC and 2 other fieldsHigh correlation
monthjan is highly overall correlated with FFMC and 1 other fieldsHigh correlation
monthjul is highly overall correlated with DC and 1 other fieldsHigh correlation
monthjun is highly overall correlated with DC and 1 other fieldsHigh correlation
monthmar is highly overall correlated with DMC and 2 other fieldsHigh correlation
monthmay is highly overall correlated with monthHigh correlation
monthnov is highly overall correlated with monthHigh correlation
monthoct is highly overall correlated with monthHigh correlation
monthsep is highly overall correlated with DMC and 3 other fieldsHigh correlation
daywed is highly imbalanced (51.7%)Imbalance
monthapr is highly imbalanced (87.3%)Imbalance
monthdec is highly imbalanced (87.3%)Imbalance
monthfeb is highly imbalanced (76.4%)Imbalance
monthjan is highly imbalanced (96.3%)Imbalance
monthjul is highly imbalanced (66.5%)Imbalance
monthjun is highly imbalanced (79.1%)Imbalance
monthmar is highly imbalanced (51.7%)Imbalance
monthmay is highly imbalanced (96.3%)Imbalance
monthnov is highly imbalanced (98.0%)Imbalance
monthoct is highly imbalanced (81.1%)Imbalance
rain has 509 (98.5%) zerosZeros
area has 247 (47.8%) zerosZeros

Reproduction

Analysis started2023-07-17 06:15:22.650203
Analysis finished2023-07-17 06:16:00.663409
Duration38.01 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

month
Categorical

Distinct12
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
aug
184 
sep
172 
mar
54 
jul
32 
feb
20 
Other values (7)
55 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1551
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowmar
2nd rowoct
3rd rowoct
4th rowmar
5th rowmar

Common Values

ValueCountFrequency (%)
aug 184
35.6%
sep 172
33.3%
mar 54
 
10.4%
jul 32
 
6.2%
feb 20
 
3.9%
jun 17
 
3.3%
oct 15
 
2.9%
apr 9
 
1.7%
dec 9
 
1.7%
jan 2
 
0.4%
Other values (2) 3
 
0.6%

Length

2023-07-17T11:46:00.835680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aug 184
35.6%
sep 172
33.3%
mar 54
 
10.4%
jul 32
 
6.2%
feb 20
 
3.9%
jun 17
 
3.3%
oct 15
 
2.9%
apr 9
 
1.7%
dec 9
 
1.7%
jan 2
 
0.4%
Other values (2) 3
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 251
16.2%
u 233
15.0%
e 201
13.0%
g 184
11.9%
p 181
11.7%
s 172
11.1%
r 63
 
4.1%
m 56
 
3.6%
j 51
 
3.3%
l 32
 
2.1%
Other values (9) 127
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1551
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 251
16.2%
u 233
15.0%
e 201
13.0%
g 184
11.9%
p 181
11.7%
s 172
11.1%
r 63
 
4.1%
m 56
 
3.6%
j 51
 
3.3%
l 32
 
2.1%
Other values (9) 127
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1551
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 251
16.2%
u 233
15.0%
e 201
13.0%
g 184
11.9%
p 181
11.7%
s 172
11.1%
r 63
 
4.1%
m 56
 
3.6%
j 51
 
3.3%
l 32
 
2.1%
Other values (9) 127
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 251
16.2%
u 233
15.0%
e 201
13.0%
g 184
11.9%
p 181
11.7%
s 172
11.1%
r 63
 
4.1%
m 56
 
3.6%
j 51
 
3.3%
l 32
 
2.1%
Other values (9) 127
8.2%

day
Categorical

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
sun
95 
fri
85 
sat
84 
mon
74 
tue
64 
Other values (2)
115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1551
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfri
2nd rowtue
3rd rowsat
4th rowfri
5th rowsun

Common Values

ValueCountFrequency (%)
sun 95
18.4%
fri 85
16.4%
sat 84
16.2%
mon 74
14.3%
tue 64
12.4%
thu 61
11.8%
wed 54
10.4%

Length

2023-07-17T11:46:01.149857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:01.559013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
sun 95
18.4%
fri 85
16.4%
sat 84
16.2%
mon 74
14.3%
tue 64
12.4%
thu 61
11.8%
wed 54
10.4%

Most occurring characters

ValueCountFrequency (%)
u 220
14.2%
t 209
13.5%
s 179
11.5%
n 169
10.9%
e 118
7.6%
f 85
 
5.5%
r 85
 
5.5%
i 85
 
5.5%
a 84
 
5.4%
m 74
 
4.8%
Other values (4) 243
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1551
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 220
14.2%
t 209
13.5%
s 179
11.5%
n 169
10.9%
e 118
7.6%
f 85
 
5.5%
r 85
 
5.5%
i 85
 
5.5%
a 84
 
5.4%
m 74
 
4.8%
Other values (4) 243
15.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1551
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 220
14.2%
t 209
13.5%
s 179
11.5%
n 169
10.9%
e 118
7.6%
f 85
 
5.5%
r 85
 
5.5%
i 85
 
5.5%
a 84
 
5.4%
m 74
 
4.8%
Other values (4) 243
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 220
14.2%
t 209
13.5%
s 179
11.5%
n 169
10.9%
e 118
7.6%
f 85
 
5.5%
r 85
 
5.5%
i 85
 
5.5%
a 84
 
5.4%
m 74
 
4.8%
Other values (4) 243
15.7%

FFMC
Real number (ℝ)

Distinct106
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.644681
Minimum18.7
Maximum96.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-07-17T11:46:01.966696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum18.7
5-th percentile84.1
Q190.2
median91.6
Q392.9
95-th percentile95.1
Maximum96.2
Range77.5
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation5.5201108
Coefficient of variation (CV)0.060898343
Kurtosis67.066041
Mean90.644681
Median Absolute Deviation (MAD)1.3
Skewness-6.575606
Sum46863.3
Variance30.471624
MonotonicityNot monotonic
2023-07-17T11:46:02.421790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92.1 28
 
5.4%
91.6 28
 
5.4%
91 22
 
4.3%
91.7 19
 
3.7%
92.4 16
 
3.1%
93.7 16
 
3.1%
92.5 15
 
2.9%
94.8 14
 
2.7%
90.1 12
 
2.3%
92.9 12
 
2.3%
Other values (96) 335
64.8%
ValueCountFrequency (%)
18.7 1
 
0.2%
50.4 1
 
0.2%
53.4 1
 
0.2%
63.5 2
0.4%
68.2 1
 
0.2%
69 1
 
0.2%
75.1 2
0.4%
79.5 3
0.6%
81.5 2
0.4%
81.6 4
0.8%
ValueCountFrequency (%)
96.2 2
 
0.4%
96.1 6
1.2%
96 2
 
0.4%
95.9 2
 
0.4%
95.8 1
 
0.2%
95.5 2
 
0.4%
95.2 7
1.4%
95.1 5
1.0%
95 1
 
0.2%
94.9 3
0.6%

DMC
Real number (ℝ)

Distinct215
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.87234
Minimum1.1
Maximum291.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-07-17T11:46:02.957217image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile14.92
Q168.6
median108.3
Q3142.4
95-th percentile231.1
Maximum291.3
Range290.2
Interquartile range (IQR)73.8

Descriptive statistics

Standard deviation64.046482
Coefficient of variation (CV)0.57765969
Kurtosis0.20482178
Mean110.87234
Median Absolute Deviation (MAD)34.9
Skewness0.54749779
Sum57321
Variance4101.9519
MonotonicityNot monotonic
2023-07-17T11:46:03.443746image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 10
 
1.9%
129.5 9
 
1.7%
231.1 8
 
1.5%
142.4 8
 
1.5%
35.8 7
 
1.4%
126.5 7
 
1.4%
108.4 7
 
1.4%
108.3 7
 
1.4%
137 7
 
1.4%
152.6 6
 
1.2%
Other values (205) 441
85.3%
ValueCountFrequency (%)
1.1 1
0.2%
2.4 1
0.2%
3 2
0.4%
3.2 1
0.2%
3.6 1
0.2%
3.7 1
0.2%
4.4 2
0.4%
4.6 1
0.2%
4.9 1
0.2%
6.6 1
0.2%
ValueCountFrequency (%)
291.3 1
 
0.2%
290 4
0.8%
287.2 1
 
0.2%
284.9 1
 
0.2%
276.3 4
0.8%
273.8 2
0.4%
269.8 1
 
0.2%
266.2 1
 
0.2%
263.1 1
 
0.2%
253.6 1
 
0.2%

DC
Real number (ℝ)

Distinct219
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean547.94004
Minimum7.9
Maximum860.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-07-17T11:46:03.898749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum7.9
5-th percentile43.58
Q1437.7
median664.2
Q3713.9
95-th percentile795.3
Maximum860.6
Range852.7
Interquartile range (IQR)276.2

Descriptive statistics

Standard deviation248.06619
Coefficient of variation (CV)0.45272507
Kurtosis-0.24524352
Mean547.94004
Median Absolute Deviation (MAD)80.2
Skewness-1.1004451
Sum283285
Variance61536.835
MonotonicityNot monotonic
2023-07-17T11:46:04.833234image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
745.3 10
 
1.9%
692.6 9
 
1.7%
692.3 8
 
1.5%
715.1 8
 
1.5%
698.6 8
 
1.5%
601.4 8
 
1.5%
80.8 7
 
1.4%
647.1 7
 
1.4%
764 7
 
1.4%
706.4 7
 
1.4%
Other values (209) 438
84.7%
ValueCountFrequency (%)
7.9 1
 
0.2%
9.3 1
 
0.2%
15.3 1
 
0.2%
15.5 1
 
0.2%
15.8 1
 
0.2%
16.2 2
0.4%
18.7 1
 
0.2%
25.6 3
0.6%
26.6 1
 
0.2%
28.3 2
0.4%
ValueCountFrequency (%)
860.6 1
 
0.2%
855.3 4
0.8%
849.3 1
 
0.2%
844 1
 
0.2%
825.1 4
0.8%
822.8 1
 
0.2%
819.1 2
0.4%
817.5 1
 
0.2%
812.1 2
0.4%
811.2 1
 
0.2%

ISI
Real number (ℝ)

Distinct119
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0216634
Minimum0
Maximum56.1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-07-17T11:46:05.235377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q16.5
median8.4
Q310.8
95-th percentile17
Maximum56.1
Range56.1
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation4.5594772
Coefficient of variation (CV)0.50539207
Kurtosis21.458037
Mean9.0216634
Median Absolute Deviation (MAD)2.1
Skewness2.5363253
Sum4664.2
Variance20.788832
MonotonicityNot monotonic
2023-07-17T11:46:05.635900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.6 23
 
4.4%
7.1 21
 
4.1%
6.3 20
 
3.9%
8.4 17
 
3.3%
7 17
 
3.3%
6.2 16
 
3.1%
9.2 15
 
2.9%
7.5 14
 
2.7%
9 12
 
2.3%
8.1 12
 
2.3%
Other values (109) 350
67.7%
ValueCountFrequency (%)
0 1
 
0.2%
0.4 2
 
0.4%
0.7 1
 
0.2%
0.8 3
0.6%
1.1 1
 
0.2%
1.5 1
 
0.2%
1.8 1
 
0.2%
1.9 6
1.2%
2 1
 
0.2%
2.1 2
 
0.4%
ValueCountFrequency (%)
56.1 1
 
0.2%
22.7 1
 
0.2%
22.6 1
 
0.2%
21.3 1
 
0.2%
20.3 4
0.8%
20 2
 
0.4%
18 4
0.8%
17.9 3
0.6%
17.7 5
1.0%
17 7
1.4%

temp
Real number (ℝ)

Distinct192
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.889168
Minimum2.2
Maximum33.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-07-17T11:46:06.052291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile8.2
Q115.5
median19.3
Q322.8
95-th percentile27.9
Maximum33.3
Range31.1
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation5.8066253
Coefficient of variation (CV)0.30740503
Kurtosis0.13616551
Mean18.889168
Median Absolute Deviation (MAD)3.6
Skewness-0.33117224
Sum9765.7
Variance33.716898
MonotonicityNot monotonic
2023-07-17T11:46:06.445329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.4 8
 
1.5%
19.6 8
 
1.5%
15.4 7
 
1.4%
20.6 7
 
1.4%
20.4 6
 
1.2%
21.9 6
 
1.2%
19.1 6
 
1.2%
15.9 6
 
1.2%
16.8 6
 
1.2%
20.1 6
 
1.2%
Other values (182) 451
87.2%
ValueCountFrequency (%)
2.2 1
 
0.2%
4.2 1
 
0.2%
4.6 6
1.2%
4.8 1
 
0.2%
5.1 5
1.0%
5.2 1
 
0.2%
5.3 3
0.6%
5.5 1
 
0.2%
5.8 2
 
0.4%
6.7 1
 
0.2%
ValueCountFrequency (%)
33.3 1
 
0.2%
33.1 1
 
0.2%
32.6 1
 
0.2%
32.4 2
0.4%
32.3 1
 
0.2%
31 1
 
0.2%
30.8 2
0.4%
30.6 1
 
0.2%
30.2 3
0.6%
29.6 1
 
0.2%

RH
Real number (ℝ)

Distinct75
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.288201
Minimum15
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-07-17T11:46:06.901487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile24
Q133
median42
Q353
95-th percentile77
Maximum100
Range85
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.317469
Coefficient of variation (CV)0.36843829
Kurtosis0.43818286
Mean44.288201
Median Absolute Deviation (MAD)10
Skewness0.86290401
Sum22897
Variance266.2598
MonotonicityNot monotonic
2023-07-17T11:46:07.310395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 33
 
6.4%
39 24
 
4.6%
35 20
 
3.9%
43 17
 
3.3%
42 17
 
3.3%
45 16
 
3.1%
34 16
 
3.1%
33 15
 
2.9%
40 15
 
2.9%
46 14
 
2.7%
Other values (65) 330
63.8%
ValueCountFrequency (%)
15 2
 
0.4%
17 1
 
0.2%
18 1
 
0.2%
19 4
 
0.8%
20 1
 
0.2%
21 7
1.4%
22 5
 
1.0%
24 13
2.5%
25 10
1.9%
26 6
1.2%
ValueCountFrequency (%)
100 1
0.2%
99 1
0.2%
97 1
0.2%
96 1
0.2%
94 1
0.2%
90 2
0.4%
88 1
0.2%
87 1
0.2%
86 2
0.4%
84 1
0.2%

wind
Real number (ℝ)

Distinct21
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0176015
Minimum0.4
Maximum9.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-07-17T11:46:07.672743image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.3
Q12.7
median4
Q34.9
95-th percentile7.6
Maximum9.4
Range9
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.7916526
Coefficient of variation (CV)0.44595079
Kurtosis0.054323817
Mean4.0176015
Median Absolute Deviation (MAD)1.3
Skewness0.57100113
Sum2077.1
Variance3.210019
MonotonicityNot monotonic
2023-07-17T11:46:07.987673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3.1 53
10.3%
2.2 53
10.3%
4 51
9.9%
4.9 48
9.3%
2.7 44
8.5%
5.4 41
7.9%
4.5 41
7.9%
3.6 40
7.7%
1.8 31
 
6.0%
5.8 24
 
4.6%
Other values (11) 91
17.6%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.9 13
 
2.5%
1.3 14
 
2.7%
1.8 31
6.0%
2.2 53
10.3%
2.7 44
8.5%
3.1 53
10.3%
3.6 40
7.7%
4 51
9.9%
4.5 41
7.9%
ValueCountFrequency (%)
9.4 4
 
0.8%
8.9 1
 
0.2%
8.5 8
 
1.5%
8 5
 
1.0%
7.6 14
 
2.7%
7.2 4
 
0.8%
6.7 8
 
1.5%
6.3 19
3.7%
5.8 24
4.6%
5.4 41
7.9%

rain
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021663443
Minimum0
Maximum6.4
Zeros509
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-07-17T11:46:08.286072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.4
Range6.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29595912
Coefficient of variation (CV)13.661684
Kurtosis421.29596
Mean0.021663443
Median Absolute Deviation (MAD)0
Skewness19.816344
Sum11.2
Variance0.087591801
MonotonicityNot monotonic
2023-07-17T11:46:08.553381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 509
98.5%
0.2 2
 
0.4%
0.8 2
 
0.4%
1 1
 
0.2%
6.4 1
 
0.2%
0.4 1
 
0.2%
1.4 1
 
0.2%
ValueCountFrequency (%)
0 509
98.5%
0.2 2
 
0.4%
0.4 1
 
0.2%
0.8 2
 
0.4%
1 1
 
0.2%
1.4 1
 
0.2%
6.4 1
 
0.2%
ValueCountFrequency (%)
6.4 1
 
0.2%
1.4 1
 
0.2%
1 1
 
0.2%
0.8 2
 
0.4%
0.4 1
 
0.2%
0.2 2
 
0.4%
0 509
98.5%

area
Real number (ℝ)

Distinct251
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.847292
Minimum0
Maximum1090.84
Zeros247
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-07-17T11:46:08.946751image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.52
Q36.57
95-th percentile48.714
Maximum1090.84
Range1090.84
Interquartile range (IQR)6.57

Descriptive statistics

Standard deviation63.655818
Coefficient of variation (CV)4.9548043
Kurtosis194.14072
Mean12.847292
Median Absolute Deviation (MAD)0.52
Skewness12.846934
Sum6642.05
Variance4052.0632
MonotonicityNot monotonic
2023-07-17T11:46:09.386402image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 247
47.8%
1.94 3
 
0.6%
0.52 2
 
0.4%
3.71 2
 
0.4%
0.68 2
 
0.4%
6.43 2
 
0.4%
2.14 2
 
0.4%
1.95 2
 
0.4%
2.18 2
 
0.4%
1.75 2
 
0.4%
Other values (241) 251
48.5%
ValueCountFrequency (%)
0 247
47.8%
0.09 1
 
0.2%
0.17 1
 
0.2%
0.21 1
 
0.2%
0.24 1
 
0.2%
0.33 1
 
0.2%
0.36 1
 
0.2%
0.41 1
 
0.2%
0.43 2
 
0.4%
0.47 1
 
0.2%
ValueCountFrequency (%)
1090.84 1
0.2%
746.28 1
0.2%
278.53 1
0.2%
212.88 1
0.2%
200.94 1
0.2%
196.48 1
0.2%
185.76 1
0.2%
174.63 1
0.2%
154.88 1
0.2%
105.66 1
0.2%

dayfri
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
432 
1
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 432
83.6%
1 85
 
16.4%

Length

2023-07-17T11:46:09.750022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:10.095343image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 432
83.6%
1 85
 
16.4%

Most occurring characters

ValueCountFrequency (%)
0 432
83.6%
1 85
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 432
83.6%
1 85
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 432
83.6%
1 85
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 432
83.6%
1 85
 
16.4%

daymon
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
443 
1
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 443
85.7%
1 74
 
14.3%

Length

2023-07-17T11:46:10.368411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:10.694686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 443
85.7%
1 74
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 443
85.7%
1 74
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 443
85.7%
1 74
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 443
85.7%
1 74
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 443
85.7%
1 74
 
14.3%

daysat
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
433 
1
84 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 433
83.8%
1 84
 
16.2%

Length

2023-07-17T11:46:10.996153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:11.339866image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 433
83.8%
1 84
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 433
83.8%
1 84
 
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 433
83.8%
1 84
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 433
83.8%
1 84
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 433
83.8%
1 84
 
16.2%

daysun
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
422 
1
95 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 422
81.6%
1 95
 
18.4%

Length

2023-07-17T11:46:11.637481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:11.956291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 422
81.6%
1 95
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0 422
81.6%
1 95
 
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 422
81.6%
1 95
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 422
81.6%
1 95
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 422
81.6%
1 95
 
18.4%

daythu
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
456 
1
61 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 456
88.2%
1 61
 
11.8%

Length

2023-07-17T11:46:12.238990image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:12.569137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 456
88.2%
1 61
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 456
88.2%
1 61
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 456
88.2%
1 61
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 456
88.2%
1 61
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 456
88.2%
1 61
 
11.8%

daytue
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
453 
1
64 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 453
87.6%
1 64
 
12.4%

Length

2023-07-17T11:46:12.855076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:13.181119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 453
87.6%
1 64
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 453
87.6%
1 64
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 453
87.6%
1 64
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 453
87.6%
1 64
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 453
87.6%
1 64
 
12.4%

daywed
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
463 
1
54 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

Length

2023-07-17T11:46:13.463844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:13.793476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

Most occurring characters

ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

monthapr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
508 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

Length

2023-07-17T11:46:14.060580image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:14.390200image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

monthaug
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
333 
1
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 333
64.4%
1 184
35.6%

Length

2023-07-17T11:46:14.656818image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:14.971286image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 333
64.4%
1 184
35.6%

Most occurring characters

ValueCountFrequency (%)
0 333
64.4%
1 184
35.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 333
64.4%
1 184
35.6%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 333
64.4%
1 184
35.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 333
64.4%
1 184
35.6%

monthdec
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
508 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

Length

2023-07-17T11:46:15.261165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:15.583788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 508
98.3%
1 9
 
1.7%

monthfeb
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
497 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 497
96.1%
1 20
 
3.9%

Length

2023-07-17T11:46:15.866609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:16.198505image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 497
96.1%
1 20
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 497
96.1%
1 20
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 497
96.1%
1 20
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 497
96.1%
1 20
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 497
96.1%
1 20
 
3.9%

monthjan
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
515 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

Length

2023-07-17T11:46:16.481857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:16.811617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

monthjul
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
485 
1
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 485
93.8%
1 32
 
6.2%

Length

2023-07-17T11:46:17.094436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:17.424148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 485
93.8%
1 32
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 485
93.8%
1 32
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 485
93.8%
1 32
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 485
93.8%
1 32
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 485
93.8%
1 32
 
6.2%

monthjun
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
500 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 500
96.7%
1 17
 
3.3%

Length

2023-07-17T11:46:17.706999image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:18.036727image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 500
96.7%
1 17
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 500
96.7%
1 17
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 500
96.7%
1 17
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 500
96.7%
1 17
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 500
96.7%
1 17
 
3.3%

monthmar
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
463 
1
54 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

Length

2023-07-17T11:46:18.303308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:18.632911image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

Most occurring characters

ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 463
89.6%
1 54
 
10.4%

monthmay
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
515 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

Length

2023-07-17T11:46:18.915702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:19.246373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 515
99.6%
1 2
 
0.4%

monthnov
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
516 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 516
99.8%
1 1
 
0.2%

Length

2023-07-17T11:46:19.529753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:19.859412image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 516
99.8%
1 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 516
99.8%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 516
99.8%
1 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 516
99.8%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 516
99.8%
1 1
 
0.2%

monthoct
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
502 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 502
97.1%
1 15
 
2.9%

Length

2023-07-17T11:46:20.126560image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:20.440553image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 502
97.1%
1 15
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 502
97.1%
1 15
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 502
97.1%
1 15
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 502
97.1%
1 15
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 502
97.1%
1 15
 
2.9%

monthsep
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
345 
1
172 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters517
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 345
66.7%
1 172
33.3%

Length

2023-07-17T11:46:20.833362image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:21.148024image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 345
66.7%
1 172
33.3%

Most occurring characters

ValueCountFrequency (%)
0 345
66.7%
1 172
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 517
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 345
66.7%
1 172
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 345
66.7%
1 172
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 345
66.7%
1 172
33.3%

size_category
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
small
378 
large
139 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2585
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsmall
2nd rowsmall
3rd rowsmall
4th rowsmall
5th rowsmall

Common Values

ValueCountFrequency (%)
small 378
73.1%
large 139
 
26.9%

Length

2023-07-17T11:46:21.430539image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:46:21.760392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
small 378
73.1%
large 139
 
26.9%

Most occurring characters

ValueCountFrequency (%)
l 895
34.6%
a 517
20.0%
s 378
14.6%
m 378
14.6%
r 139
 
5.4%
g 139
 
5.4%
e 139
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2585
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 895
34.6%
a 517
20.0%
s 378
14.6%
m 378
14.6%
r 139
 
5.4%
g 139
 
5.4%
e 139
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2585
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 895
34.6%
a 517
20.0%
s 378
14.6%
m 378
14.6%
r 139
 
5.4%
g 139
 
5.4%
e 139
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 895
34.6%
a 517
20.0%
s 378
14.6%
m 378
14.6%
r 139
 
5.4%
g 139
 
5.4%
e 139
 
5.4%

Interactions

2023-07-17T11:45:55.810519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:32.496125image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:35.716643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:38.530226image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:42.045547image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:44.739944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:47.478762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:50.185940image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:52.909441image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:56.142908image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:33.028780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:36.065065image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:38.851974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:42.376009image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:45.088562image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:47.808910image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:50.517873image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:53.270468image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:56.456591image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:33.373889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:36.380476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:39.151404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:42.674717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:45.389811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:48.107435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:50.838401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:53.663285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:56.738899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:33.780760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:36.679655image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:40.299239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:42.957377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:45.689298image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:48.397947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:51.117460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:53.964241image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:57.052649image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:34.115227image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:36.978262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:40.588004image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:43.258065image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:45.989838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:48.738181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:51.400807image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:54.273718image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:57.366238image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:34.441217image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:37.293978image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:40.880472image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:43.558504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:46.281651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:49.036710image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:51.714790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:54.582042image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:57.648692image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:34.753983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:37.593265image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:41.163680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:43.843268image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:46.564860image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:49.316919image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:52.001656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:54.892172image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:57.931017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:35.063015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:37.891636image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:41.447483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:44.128798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:46.852387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:49.596920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:52.302020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:55.184290image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:58.244339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:35.396549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:38.223255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:41.746629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:44.441166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:47.163012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:49.897844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:52.610988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-17T11:45:55.499960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-17T11:46:22.121622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
FFMCDMCDCISItempRHwindrainareamonthdaydayfridaymondaysatdaysundaythudaytuedaywedmonthaprmonthaugmonthdecmonthfebmonthjanmonthjulmonthjunmonthmarmonthmaymonthnovmonthoctmonthsepsize_category
FFMC1.0000.5110.2630.7840.595-0.320-0.0350.0970.0250.4420.0880.0000.1350.1360.0700.0000.1780.0000.2310.2230.3250.5140.7030.0000.1300.1570.0000.4330.0000.1860.000
DMC0.5111.0000.5590.4250.5030.035-0.1100.1210.0720.4100.1080.0210.0800.1020.1300.1930.1450.0000.3320.5740.3320.5230.1030.1790.0990.6140.1030.0000.4450.5600.096
DC0.2630.5591.0000.1040.3090.026-0.2060.0080.0620.5540.0910.1010.0000.0630.0810.0710.1470.1300.2800.6260.5760.4740.1000.5900.8070.7520.1000.1680.1210.6950.000
ISI0.7840.4250.1041.0000.416-0.1770.1360.1170.0120.3050.1540.1940.1470.1160.0850.0680.1960.1160.2100.3810.2450.3930.1030.0000.2190.1460.0000.0210.0170.2710.000
temp0.5950.5030.3090.4161.000-0.518-0.1800.0260.0790.3370.0680.1060.1350.0280.0000.0280.0000.0950.2030.3180.6890.3720.3010.1210.0000.4430.0000.0440.0000.2160.106
RH-0.3200.0350.026-0.177-0.5181.0000.0370.181-0.0240.1820.0710.0410.0000.1580.1240.0680.0000.0000.0000.1230.2750.1410.3230.0000.0950.1460.2890.0000.0000.0000.000
wind-0.035-0.110-0.2060.136-0.1800.0371.0000.1210.0530.2250.0960.1340.1590.0000.1280.0000.0870.1090.1270.0000.6300.0790.0420.0000.0970.1700.0000.0000.0660.2010.108
rain0.0970.1210.0080.1170.0260.1810.1211.000-0.0640.0000.0500.0750.0000.0000.0000.0000.1550.0000.0000.1090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.048
area0.0250.0720.0620.0120.079-0.0240.053-0.0641.0000.0000.0290.0000.0800.1140.0000.0940.0000.0000.0000.0000.0000.0000.0000.1500.0000.0000.0000.0000.0000.0000.201
month0.4420.4100.5540.3050.3370.1820.2250.0000.0001.0000.0000.0740.1220.0000.0000.0000.0850.0000.9900.9900.9900.9900.9900.9900.9900.9900.9900.9900.9900.9900.150
day0.0880.1080.0910.1540.0680.0710.0960.0500.0290.0001.0000.9950.9950.9950.9950.9950.9950.9950.0000.1630.0580.0000.0000.0000.0000.0000.0000.0460.0000.0750.000
dayfri0.0000.0210.1010.1940.1060.0410.1340.0750.0000.0740.9951.0000.1680.1830.1990.1480.1530.1360.0000.0850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.000
daymon0.1350.0800.0000.1470.1350.0000.1590.0000.0800.1220.9950.1681.0000.1670.1820.1340.1390.1230.0000.1170.0820.0000.0000.0000.0000.0520.0000.0000.0070.0000.000
daysat0.1360.1020.0630.1160.0280.1580.0000.0000.1140.0000.9950.1830.1671.0000.1980.1470.1510.1350.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.000
daysun0.0700.1300.0810.0850.0000.1240.1280.0000.0000.0000.9950.1990.1820.1981.0000.1600.1650.1480.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
daythu0.0000.1930.0710.0680.0280.0680.0000.0000.0940.0000.9950.1480.1340.1470.1601.0000.1210.1060.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.000
daytue0.1780.1450.1470.1960.0000.0000.0870.1550.0000.0850.9950.1530.1390.1510.1650.1211.0000.1100.0000.0380.0000.0000.0000.0000.0290.0000.0000.0240.0000.0000.000
daywed0.0000.0000.1300.1160.0950.0000.1090.0000.0000.0000.9950.1360.1230.1350.1480.1060.1101.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.000
monthapr0.2310.3320.2800.2100.2030.0000.1270.0000.0000.9900.0000.0000.0000.0000.0000.0000.0000.0001.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.000
monthaug0.2230.5740.6260.3810.3180.1230.0000.1090.0000.9900.1630.0850.1170.0000.0400.0180.0380.0540.0711.0000.0710.1320.0000.1770.1180.2440.0000.0000.1080.5190.032
monthdec0.3250.3320.5760.2450.6890.2750.6300.0000.0000.9900.0580.0000.0820.0000.0000.0000.0000.0000.0000.0711.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.164
monthfeb0.5140.5230.4740.3930.3720.1410.0790.0000.0000.9900.0000.0000.0000.0000.0000.0000.0000.0000.0000.1320.0001.0000.0000.0000.0000.0280.0000.0000.0000.1230.000
monthjan0.7030.1030.1000.1030.3010.3230.0420.0000.0000.9900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
monthjul0.0000.1790.5900.0000.1210.0000.0000.0000.1500.9900.0000.0000.0000.0240.0000.0000.0000.0000.0000.1770.0000.0000.0001.0000.0000.0600.0000.0000.0000.1670.000
monthjun0.1300.0990.8070.2190.0000.0950.0970.0000.0000.9900.0000.0000.0000.0000.0000.0000.0290.0000.0000.1180.0000.0000.0000.0001.0000.0100.0000.0000.0000.1100.000
monthmar0.1570.6140.7520.1460.4430.1460.1700.0000.0000.9900.0000.0000.0520.0000.0000.0000.0000.0000.0000.2440.0000.0280.0000.0600.0101.0000.0000.0000.0000.2300.000
monthmay0.0000.1030.1000.0000.0000.2890.0000.0000.0000.9900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
monthnov0.4330.0000.1680.0210.0440.0000.0000.0000.0000.9900.0460.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
monthoct0.0000.4450.1210.0170.0000.0000.0660.0000.0000.9900.0000.0000.0070.0000.0000.0110.0000.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.0001.0000.1010.000
monthsep0.1860.5600.6950.2710.2160.0000.2010.0000.0000.9900.0750.0920.0000.0000.0000.0000.0000.0150.0650.5190.0650.1230.0000.1670.1100.2300.0000.0000.1011.0000.000
size_category0.0000.0960.0000.0000.1060.0000.1080.0480.2010.1500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.1640.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-07-17T11:45:58.825382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-17T11:46:00.176455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

monthdayFFMCDMCDCISItempRHwindrainareadayfridaymondaysatdaysundaythudaytuedaywedmonthaprmonthaugmonthdecmonthfebmonthjanmonthjulmonthjunmonthmarmonthmaymonthnovmonthoctmonthsepsize_category
0marfri86.226.294.35.18.2516.70.00.01000000000000010000small
1octtue90.635.4669.16.718.0330.90.00.00000010000000000010small
2octsat90.643.7686.96.714.6331.30.00.00010000000000000010small
3marfri91.733.377.59.08.3974.00.20.01000000000000010000small
4marsun89.351.3102.29.611.4991.80.00.00001000000000010000small
5augsun92.385.3488.014.722.2295.40.00.00001000010000000000small
6augmon92.388.9495.68.524.1273.10.00.00100000010000000000small
7augmon91.5145.4608.210.78.0862.20.00.00100000010000000000small
8septue91.0129.5692.67.013.1635.40.00.00000010000000000001small
9sepsat92.588.0698.67.122.8404.00.00.00010000000000000001small
monthdayFFMCDMCDCISItempRHwindrainareadayfridaymondaysatdaysundaythudaytuedaywedmonthaprmonthaugmonthdecmonthfebmonthjanmonthjulmonthjunmonthmarmonthmaymonthnovmonthoctmonthsepsize_category
507augfri91.0166.9752.67.125.9413.60.00.001000000010000000000small
508augfri91.0166.9752.67.125.9413.60.00.001000000010000000000small
509augfri91.0166.9752.67.121.1717.61.42.171000000010000000000small
510augfri91.0166.9752.67.118.2625.40.00.431000000010000000000small
511augsun81.656.7665.61.927.8352.70.00.000001000010000000000small
512augsun81.656.7665.61.927.8322.70.06.440001000010000000000large
513augsun81.656.7665.61.921.9715.80.054.290001000010000000000large
514augsun81.656.7665.61.921.2706.70.011.160001000010000000000large
515augsat94.4146.0614.711.325.6424.00.00.000010000010000000000small
516novtue79.53.0106.71.111.8314.50.00.000000010000000000100small

Duplicate rows

Most frequently occurring

monthdayFFMCDMCDCISItempRHwindrainareadayfridaymondaysatdaysundaythudaytuedaywedmonthaprmonthaugmonthdecmonthfebmonthjanmonthjulmonthjunmonthmarmonthmaymonthnovmonthoctmonthsepsize_category# duplicates
0augfri91.0166.9752.67.125.9413.60.00.001000000010000000000small2
1augsat93.7231.1715.18.418.9644.90.00.000010000010000000000small2
2augsun91.4142.4601.410.619.8395.40.00.000001000010000000000small2
3augthu91.6248.4753.86.320.4562.20.00.000000100010000000000small2
4augtue96.1181.1671.214.321.6654.90.80.000000010010000000000small2
5augwed92.1111.2654.19.620.4424.90.00.000000001010000000000small2
6junfri91.194.1232.17.119.2384.50.00.001000000000000100000small2
7marsat91.735.880.87.817.0274.90.028.660010000000000010000large2